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Sagarin College Football Rankings!

Atlanta Griz1 said:
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

Yeah right! Please tell us that the way the football bounces is inversely proportional to the number of dimples on the ball. God, you psychologists provide funny sh*t to our world!
I'm not sure I want to explain stochastic variation in a logistical regression to you. You barely understand football.
 
This is one reason we want NDSU to be as good as predicted and now have a great year. When we beat App. State a few years ago we thought we were something and that the win would pay off each week with rankings as they did well. The problem was they didnt. Go NDSU.
 
grizpsych said:
Atlanta Griz1 said:
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

Yeah right! Please tell us that the way the football bounces is inversely proportional to the number of dimples on the ball. God, you psychologists provide funny sh*t to our world!
I'm not sure I want to explain stochastic variation in a logistical regression to you. You barely understand football.

I think a journal publication in the Journal Of Applied Psychology is in order...
 
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

You can quantify motivation? Emotions? Weather? Determination? Heart?
 
grizpsych said:
Atlanta Griz1 said:
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

Yeah right! Please tell us that the way the football bounces is inversely proportional to the number of dimples on the ball. God, you psychologists provide funny sh*t to our world!
I'm not sure I want to explain stochastic variation in a logistical regression to you. You barely understand football.

:lol:
 
AZGrizFan said:
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

You can quantify motivation? Emotions? Weather? Determination? Heart?
Why not? They're all variables. However, you would first need to do a factor analysis to determine which variables actually account for variance of game outcomes.

I don't get you egrizzers sometimes. What do you think goes into line creation for game odds in Vegas? Seriously, if Target can predict a girl is pregnant before her parents know, you can predict a football game with decent accuracy.

http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/
 
grizpsych said:
AZGrizFan said:
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

You can quantify motivation? Emotions? Weather? Determination? Heart?
Why not? They're all variables. However, you would first need to do a factor analysis to determine which variables actually account for variance of game outcomes.

I don't get you egrizzers sometimes. What do you think goes into line creation for game odds in Vegas? Seriously, if Target can predict a girl is pregnant before her parents know, you can predict a football game with decent accuracy.

http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

Interesting. Because I think most games are pretty easy to predict based strictly on the talent disparity and injury report (with the weather impact somewhat more problematic but less frequent). I think the reason Vegas is wrong at times is exactly BECAUSE of an inability to quantify things like heart, determination, motivation, drive, etc...either of individual players or of a team mindset.

Just because something is a variable doesn't mean its quantifiable. If it were, we wouldn't have covered against NDSU in Fargo, and we wouldn't have beaten them outright in Wa/Griz despite being a 15 point dog.

And after reading that article, I'd LOVE to hear how you'd propose datamining a football player/team for determination or heart.
 
grizpsych said:
Atlanta Griz1 said:
grizpsych said:
Atlanta Griz1 said:
Statistical modeling to predict athletic outcomes fails to take into consideration such things as.... motivation, injuries, emotion, crazy bounces of an oblong object, game officials, etc.
All of this is quantifiable.

Yeah right! Please tell us that the way the football bounces is inversely proportional to the number of dimples on the ball. God, you psychologists provide funny sh*t to our world!
I'm not sure I want to explain stochastic variation in a logistical regression to you. You barely understand football.

Your big words don't hide the fact that you went into psychology because you needed to "find yourself", and thought telling others what's wrong with them would help you do that. I've never met a psychologist yet who knew jack sh*t about football. Save your analyzing for the dopes you "analyze", and forget about regressive postulating about football. :whocares:
 
You won by 3 seconds after our coaches had the stupid idea of throwing on 2nd and 3rd down and our QB had an awful day. Deal with it, you're still about #5-6 or so in the Fcs.

All our smack talk doesn't matter anyways, we will meet again in the playoffs
 
AZGrizFan said:
grizpsych said:
AZGrizFan said:
grizpsych said:
All of this is quantifiable.

You can quantify motivation? Emotions? Weather? Determination? Heart?
Why not? They're all variables. However, you would first need to do a factor analysis to determine which variables actually account for variance of game outcomes.

I don't get you egrizzers sometimes. What do you think goes into line creation for game odds in Vegas? Seriously, if Target can predict a girl is pregnant before her parents know, you can predict a football game with decent accuracy.

http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

Interesting. Because I think most games are pretty easy to predict based strictly on the talent disparity and injury report (with the weather impact somewhat more problematic but less frequent). I think the reason Vegas is wrong at times is exactly BECAUSE of an inability to quantify things like heart, determination, motivation, drive, etc...either of individual players or of a team mindset.

Just because something is a variable doesn't mean its quantifiable. If it were, we wouldn't have covered against NDSU in Fargo, and we wouldn't have beaten them outright in Wa/Griz despite being a 15 point dog.

And after reading that article, I'd LOVE to hear how you'd propose datamining a football player/team for determination or heart.

I don't know if I would use "heart" in a predictive model for football. Although fans discuss this variable a lot, you would first have to see if it is the same variable as motivation, home field advantage etc. Thus, the first process would be operationally defining (determine the scale of measurement) each variable you hypothesize influences the teams chance of winning. Next, collect data for each variable for at least 20 games. Then, conduct a Factor analysis to ascertain if each variable uniquely accounts for the variance of scores. Here, you would also determine if your model captures a significant amount of score variance. If not, you would have to add more/different variables to the analysis and repeat previous steps (i.e., 20 games of data collection). But, once you capture 80 to 90% of score variance (really anything above 55% to make money) you could start using your model to predict scores. However, you would also want to determine the relationships between each of the variables themselves for any possible interactions (mediating/moderating effects).

With that written, I hope you can see that creating a model like this would take years. And, would have to be updated as players on each team change. Thus, this is why you do not see these variables used in traditional rating models. But, that does not mean it couldn't be done. Indeed, I'm sure that Vegas casinos have predictive analytic teams do just this. But, their models are proprietary.
 
Bisonation said:
You won by 3 seconds after our coaches had the stupid idea of throwing on 2nd and 3rd down and our QB had an awful day. Deal with it, you're still about #5-6 or so in the Fcs.

All our smack talk doesn't matter anyways, we will meet again in the playoffs

Actually it's points, we won by points not seconds. You're thinking of the 50 yard fried cheese curd dash.
 
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